Model Parameters Research Articles

Efficient random phase approximation for diradicals.

We apply the analytically solvable model of two electrons in two orbitals to diradical molecules, characterized by two unpaired electrons. The effect of doubly occupied and empty orbitals is taken into account by means of random phase approximation (RPA). We show that in the static limit, the direct RPA leads to the renormalization of the parameters of the two-orbital model. We test our model by comparing its predictions for singlet-triplet splitting with the results of several multi-reference methods for a set of thirteen molecules. We find that for this set, the static RPA results are close to those of the NEVPT2 method with two orbitals and two electrons in the active space.

Forest Canopy Height Estimation Combining Dual-Polarization PolSAR and Spaceborne LiDAR Data

Forest canopy height data are fundamental parameters of forest structure and are critical for understanding terrestrial carbon stock, global carbon cycle dynamics and forest productivity. To address the limitations of retrieving forest canopy height using conventional PolInSAR-based methods, we proposed a method to estimate forest height by combining single-temporal polarimetric synthetic aperture radar (PolSAR) images with sparse spaceborne LiDAR (forest height) measurements. The core idea of our method is that volume scattering energy variations which are linked to forest canopy height occur during radar acquisition. Specifically, our methodology begins by employing a semi-empirical inversion model directly derived from the random volume over ground (RVoG) formulation to establish the relationship between forest canopy height, volume scattering energy and wave extinction. Subsequently, PolSAR decomposition techniques are used to extract canopy volume scattering energy. Additionally, machine learning is employed to generate a spatially continuous extinction coefficient product, utilizing sparse LiDAR samples for assistance. Finally, with the derived inversion model and the resulting model parameters (i.e., volume scattering power and extinction coefficient), forest canopy height can be estimated. The performance of the proposed forest height inversion method is illustrated with L-band NASA/JPL UAVSAR from AfriSAR data conducted over the Gabon Lope National Park and airborne LiDAR data. Compared to high-accuracy airborne LiDAR data, the obtained forest canopy height from the proposed approach exhibited higher accuracy (R2 = 0.92, RMSE = 6.09 m). The results demonstrate the potential and merit of the synergistic combination of PolSAR (volume scattering power) and sparse LiDAR (forest height) measurements for forest height estimation. Additionally, our approach achieves good performance in forest height estimation, with accuracy comparable to that of the multi-baseline PolInSAR-based inversion method (RMSE = 5.80 m), surpassing traditional PolSAR-based methods with an accuracy of 10.86 m. Given the simplicity and efficiency of the proposed method, it has the potential for large-scale forest height estimation applications when only single-temporal dual-polarization acquisitions are available.

G-YOLO: A Lightweight Infrared Aerial Remote Sensing Target Detection Model for UAVs Based on YOLOv8

A lightweight infrared target detection model, G-YOLO, based on an unmanned aerial vehicle (UAV) is proposed to address the issues of low accuracy in target detection of UAV aerial images in complex ground scenarios and large network models that are difficult to apply to mobile or embedded platforms. Firstly, the YOLOv8 backbone feature extraction network is improved and designed based on the lightweight network, GhostBottleneckV2, and the remaining part of the backbone network adopts the depth-separable convolution, DWConv, to replace part of the standard convolution, which effectively retains the detection effect of the model while greatly reducing the number of model parameters and calculations. Secondly, the neck structure is improved by the ODConv module, which adopts an adaptive convolutional structure to adaptively adjust the convolutional kernel size and step size, which allows for more effective feature extraction and detection based on targets at different scales. At the same time, the neck structure is further optimized using the attention mechanism, SEAttention, to improve the model’s ability to learn global information of input feature maps, which is then applied to each channel of each feature map to enhance the useful information in a specific channel and improve the model’s detection performance. Finally, the introduction of the SlideLoss loss function enables the model to calculate the differences between predicted and actual truth bounding boxes during the training process, and adjust the model parameters based on these differences to improve the accuracy and efficiency of object detection. The experimental results show that compared with YOLOv8n, the G-YOLO reduces the missed and false detection rates of infrared small target detection in complex backgrounds. The number of model parameters is reduced by 74.2%, the number of computational floats is reduced by 54.3%, the FPS is improved by 71, which improves the detection efficiency of the model, and the average accuracy (mAP) reaches 91.4%, which verifies the validity of the model for UAV-based infrared small target detection. Furthermore, the FPS of the model reaches 556, and it will be suitable for wider and more complex detection task such as small targets, long-distance targets, and other complex scenes.

Conducting power analysis for meta-analysis with dependent effect sizes: Common guidelines and an introduction to the POMADE R package.

Sample size and statistical power are important factors to consider when planning a research synthesis. Power analysis methods have been developed for fixed effect or random effects models, but until recently these methods were limited to simple data structures with a single, independent effect per study. Recent work has provided power approximation formulas for meta-analyses involving studies with multiple, dependent effect size estimates, which are common in syntheses of social science research. Prior work focused on developing and validating the approximations but did not address the practice challenges encountered in applying them for purposes of planning a synthesis involving dependent effect sizes. We aim to facilitate the application of these recent developments by providing practical guidance on how to conduct power analysis for planning a meta-analysis of dependent effect sizes and by introducing a new R package, POMADE, designed for this purpose. We present a comprehensive overview of resources for finding information about the study design features and model parameters needed to conduct power analysis, along with detailed worked examples using the POMADE package. For presenting power analysis findings, we emphasize graphical tools that can depict power under a range of plausible assumptions and introduce a novel plot, the traffic light power plot, for conveying the degree of certainty in one's assumptions.

Requirements for the Development and Operation of a Freeze-Up Ice-Jam Flood Forecasting System

This article provides a comprehensive overview of ice-jam flood forecasting methodologies applicable to rivers during freezing. It emphasizes the importance of understanding river ice processes and fluvial geomorphology for developing a freeze-up ice-jam flood forecasting system. The article showcases a stochastic modelling approach, which involves simulating a deterministic river ice model multiple times with varying parameters and boundary conditions. This approach has been applied to the Exploits River at Badger in Newfoundland, Canada, a river that has experienced several freeze-up ice-jam floods. The forecasting involves two approaches: predicting the extent of the ice cover during river freezing and using an ensemble method to determine backwater flood level elevations. Other examples of current ice-jam flood forecasting systems for the Kokemäenjoki River (Pori, Finland), Saint John River (Edmundston, NB, Canada), and Churchill River (Mud Lake, NL, Canada) that are operational are also presented. The text provides a detailed explanation of the processes involved in river freeze-up and ice-jam formation, as well as the methodologies used for freeze-up ice-jam flood forecasting. Ice-jam flood forecasting systems used for freeze-up were compared to those employed for spring breakup. Spring breakup and freeze-up ice-jam flood forecasting systems differ in their driving factors and methodologies. Spring breakup, driven by snowmelt runoff, typically relies on deterministic and probabilistic approaches to predict peak flows. Freeze-up, driven by cold temperatures, focuses on the complex interactions between atmospheric conditions, river flow, and ice dynamics. Both systems require air temperature forecasts, but snowpack data are more crucial for spring breakup forecasting. To account for uncertainty, both approaches may employ ensemble forecasting techniques, generating multiple forecasts using slightly different initial conditions or model parameters. The objective of this review is to provide an overview of the current state-of-the-art in ice-jam flood forecasting systems and to identify gaps and areas for improvement in existing ice-jam flood forecasting approaches, with a focus on enhancing their accuracy, reliability, and decision-making potential. In conclusion, an effective freeze-up ice-jam flood forecasting system requires real-time data collection and analysis, historical data analysis, ice jam modeling, user interface design, alert systems, and integration with other relevant systems. This combination allows operators to better understand ice jam behavior and make informed decisions about potential risks or mitigation measures to protect people and property along rivers. The key findings of this review are as follows: (i) Ice-jam flood forecasting systems are often based on simple, empirical models that rely heavily on historical data and limited real-time monitoring information. (ii) There is a need for more sophisticated modeling techniques that can better capture the complex interactions between ice cover, water levels, and channel geometry. (iii) Combining data from multiple sources such as satellite imagery, ground-based sensors, numerical models, and machine learning algorithms can significantly improve the accuracy and reliability of ice-jam flood forecasts. (iv) Effective decision-support tools are crucial for integrating ice-jam flood forecasts into emergency response and mitigation strategies.

Continuum models for meso-scale simulations of HMX (1,3,5,7-tetranitro-1,3,5,7-tetrazocane) guided by molecular dynamics: Pore collapse, shear bands, and hotspot temperature

Meso-scale calculations of energy localization and initiation in energetic material microstructures must capture the deformation and collapse of pores and high-temperature shear bands, which lead to hotspots. Because chemical reaction rates depend sensitively on temperature, predictive continuum models need to get the pore-collapse dynamics and resulting hotspot temperatures right; this imposes stringent demands on the fidelity of thermophysical model forms and parameters and on the numerical methods employed to perform high-resolution meso-scale calculations. Here, continuum material models for β-HMX are examined in the context of shock-induced pore collapse, treating predictions from all-atom molecular dynamics (MD) simulations as ground truth. Using atomistics-consistent material properties, we show that the currently available strength models for HMX fail to correctly capture pore collapse and hotspot temperatures. Insights from MD are then employed to advance a Modified Johnson–Cook (M-JC) strength model, which is shown to capture key aspects of the physics of shock-induced localization in HMX. The study culminates in a MD-guided strength model for β-HMX that produces continuum pore-collapse results in better alignment on several aspects with those predicted by MD, including pore-collapse mechanism and rate, shear-band formation in the collapse zone, and temperature, strain, and stress fields in the hotspot zone and the surrounding material. The resulting MD-informed/MD-determined M-JC model should improve the fidelity of meso-scale simulations to predict the detonation initiation of HMX-based energetic materials in microstructure-aware multi-scale frameworks.

Failure analysis of Nehbandan granite under various stress states and strain rates using a calibrated Riedel–Hiermaier–Thoma constitutive model

This study presents a comprehensive procedure for determining, calibrating, and validating the Riedel–Hiermaier–Thoma (RHT) material model parameters for granite. The process involves collecting a comprehensive dataset of conventional mechanical tests conducted on various types of granite worldwide. Based on this dataset, one set of RHT material model parameters is determined. Additionally, a specific granite sample from Iran, known as Nehbandan granite, is characterized through physical and mechanical testing to obtain another set of parameters. The challenging task of determining the third set of parameters, which are difficult to obtain analytically and experimentally, is accomplished through a calibration process that iteratively adjusts the parameters based on comparisons between numerical simulation results and experimental data. To validate the determined parameters, a series of tests, including uniaxial compressive strength (UCS), triaxial compressive strength (TCS), Brazilian tensile strength, and dynamic Brazilian using split Hopkinson pressure bar (SHPB) tests, are conducted on the Nehbandan granite. These tests are also simulated using LS-Dyna software, and the numerical simulation results are compared with the corresponding experimental test results. The comparison between the numerical and experimental data serves as a means of validating and verifying the accuracy and reliability of the determined RHT material model parameters for granite. The results demonstrate the successful determination and calibration of the RHT material model parameters for granite. The model exhibits effectiveness in predicting the behavior of granite under various loading conditions. The validation process confirms the accuracy and reliability of the determined parameters through a close agreement between numerical simulations and experimental data. The findings contribute to a better understanding of granite's mechanical response and provide a reliable tool for simulating and predicting its behavior in engineering applications. The validated RHT material model parameters offer a robust framework for accurate numerical simulations, enabling engineers to make informed decisions in rock engineering projects.

Non-stationary inversion of seismic data for fracture compliances in azimuthally anisotropic mediag

Fractures and tectonic settings cause azimuthal anisotropy in reservoirs. Recognizing the fracture model from the seismic data is a useful tool for identifying the productive zone in the reservoirs. We applied azimuthal velocity analysis in seismic processing to improve image quality and to estimate anisotropic model parameters. Using azimuthal residual moveout analysis, we predicted the direction of azimuthal anisotropy in the reservoir, and found that the results are consistent with fracture orientations obtained from the image logs in the reservoirs. Bayes’ theorem, and a cascaded procedure in least-squares inversion, matching observed amplitudes to linearized Zoeppritz equations, were used to estimate the elastic moduli in a first step and normal and tangential fracture weaknesses in a second step. We used laboratory experiments on core samples to validate the first step inversion results. We found that the propagation wavelet varied in space and reflection time, and so a library of extracted wavelets in the time-frequency domain was used for seismic inversion. Maps of computed fracture fluid index and the estimated fracture weaknesses help to visualize the role of fractures in reservoir productivity and revealed a consistency with the seismic peak frequency attribute in identifying zones of highly compliant fracture fill. The estimated fracture model demonstrates a good fit with the fractures seen in the available core samples and implies that the fracture fluid index is a useful attribute for determining the productive zones in the reservoir.

Shanghai Transport Carbon Emission Forecasting Study Based on CEEMD-IWOA-KELM Model

In the light of the worsening of, and the adverse effects produced by, global warming, a study of Shanghai’s transport carbon emissions can provide an advanced model that can be replicated throughout other cities, thus assisting in the management and reduction of carbon emissions. Considering the volatility and nonlinearity of the carbon emission data series of the transport industry, a prediction model combining complementary ensemble empirical modal decomposition (CEEMD), the improved whale optimization algorithm (IWOA), and the Kernel Extreme Learning Machine (KELM) is proposed for a more accurate prediction of the forecasting of carbon emissions from Shanghai’s transport sector. First, nine indicators were screened as the influencing factors of Shanghai’s transport carbon emissions through the STIRPAT model, and the corresponding carbon emissions were calculated with data related to Shanghai’s transport carbon emissions from 1995 to 2019; Secondly, CEEMD was used to decompose the original data into multiple smooth series and one residual term, and KELM was applied to build a prediction model for each decomposition result, and IWOA was used to optimize the model parameters. The experimental results also demonstrate that CEEMD can effectively reduce model errors. Comparative experiments show that the IWOA algorithm can significantly enhance the stability of machine learning models. The outcomes of various experiments indicate that the CEEMD-IWOA-KELM model produces optimal results with the highest accuracy. Additionally, this model exhibits high stability, as it provides a wider range of methods for predicting carbon emissions and contributing to carbon reduction targets.

Estimation of AMOC transition probabilities using a machine learning based rare-event algorithm

Abstract The Atlantic Meridional Overturning Circulation (AMOC) is an important component of the global climate, known to be a tipping element, as it could collapse under global warming. The main objective of this study is to compute the probability that the AMOC collapses within a specified time window, using a rare-event algorithm called Trajectory-Adaptive Multilevel Splitting (TAMS). However, the efficiency and accuracy of TAMS depend on the choice of the score function. Although the definition of the optimal score function, called “committor function” is known, it is impossible in general to compute it a priori. Here, we combine TAMS with a Next-Generation Reservoir Computing technique that estimates the committor function from the data generated by the rare-event algorithm. We test this technique in a stochastic box model of the AMOC for which two types of transition exist, the so-called F(ast)-transitions and S(low)-transitions. Results for the F-transtions compare favorably with those in the literature where a physically-informed score function was used. We show that coupling a rare-event algorithm with machine learning allows for a correct estimation of transition probabilities, transition times, and even transition paths for a wide range of model parameters. We then extend these results to the more difficult problem of S-transitions in the same model. In both cases of F-transitions and S-transitions, we also show how the Next-Generation Reservoir Computing technique can be interpreted to retrieve an analytical estimate of the committor function.

Feed Forward Modeling: an efficient approach for mathematical modeling of the force frequency relationship in the rabbit isolated ventricular myocyte

Background and Objective. This study addresses the Force-Frequency relationship, a fundamental characteristic of cardiac muscle influenced by β 1-adrenergic stimulation. This relationship reveals that heart rate (HR) changes at the sinoatrial node lead to alterations in ventricular cell contractility, increasing the force and decreasing relaxation time for higher beat rates. Traditional models lacking this relationship offer an incomplete physiological depiction, impacting the interpretation of in silico experiment results. To improve this, we propose a new mathematical model for ventricular myocytes, named ‘Feed Forward Modeling’ (FFM). Methods. FFM adjusts model parameters like channel conductance and Ca2+ pump affinity according to stimulation frequency, in contrast to fixed parameter values. An empirical sigmoid curve guided the adaptation of each parameter, integrated into a rabbit ventricular cell electromechanical model. Model validation was achieved by comparing simulated data with experimental current–voltage (I-V) curves for L-type Calcium and slow Potassium currents. Results. FFM-enhanced simulations align more closely with physiological behaviors, accurately reflecting inotropic and lusitropic responses. For instance, action potential duration at 90% repolarization (APD90) decreased from 206 ms at 1 Hz to 173 ms at 4 Hz using FFM, contrary to the conventional model, where APD90 increased, limiting high-frequency heartbeats. Peak force also showed an increase with FFM, from 8.5 mN mm−2 at 1 Hz to 11.9 mN mm−2 at 4 Hz, while it barely changed without FFM. Relaxation time at 50% of maximum force (t50) similarly improved, dropping from 114 ms at 1 Hz to 75.9 ms at 4 Hz with FFM, a change not observed without the model. Conclusion. The FFM approach offers computational efficiency, bypassing the need to model all beta-adrenergic pathways, thus facilitating large-scale simulations. The study recommends that frequency change experiments include fractional dosing of isoproterenol to better replicate heart conditions in vivo.

Systematic study of theoretical estimation of nuclear reaction cross section of proton-induced reactions on Neodymium target upto 65 MeV using TALYS-1.96 code

Abstract Systematic study of theoretical estimation of nuclear cross section of charged particle induced reactions on rare earth nuclei has been carried out. The production cross section of the $^{150,149,148,146,144,143,141}$Pm and $^{149,147}$Nd nuclei were calculated theoretically via proton induced reaction on neodymium using TALYS (version 1.96) code in the default mode, with different combination of nuclear models as well as using adjusted nuclear model parameters from reaction threshold to the proton energy upto 65 MeV. The theoretically computed results were compared with the experimental results taken from EXFOR database and literature, reported by various research groups. Moreover, the effect of the various Level Density Models (LDMs), Preequilibrium Models (PEs), Optical Model Potentials (OMPs), gamma Strength Functions ($\gamma$SFs) in the cross section calculation were taken into consideration. The present theoretical analysis will help to understand the theory of nuclear reaction models and improve the evaluated nuclear data libraries.

Theoretical study of the π+p→pa0+ϕ reaction

We study the production of hyperon resonances in the π+p→pa0+ϕ reaction within an effective Lagrangian approach. The model includes the production of Δ(1940) and Δ(1920) in the intermediate state excited by the ρ and p exchanges between the initial proton and π meson. Because of the large contribution of ρ exchange in Δ(1940) production, Δ(1940) is found to be a significant contribution near the threshold in this reaction. We provide total and differential cross section predictions for the reaction and discuss the possible influence of model parameters, off-shell effects, and branching ratios for Δ(1940)→pa0 and Δ(1920)→pa0 decays, which will be useful in future experimental studies. This reaction can provide a platform for studying the features of Δ(1940) resonance, especially the coupling to the pa0 channel. Published by the American Physical Society 2024

Pressure-tuned many-body phases through Γ-K valleytronics in moiré bilayer WSe2

Abstract Recent experiments in twisted bilayer transition-metal dichalcogenides have revealed a variety of strongly correlated phenomena. To theoretically explore their origin, we combine here ab initio calculations with correlated model approaches to describe and study many-body effects in twisted bilayer WSe2 under pressure. We find that the interlayer distance is a key factor for the electronic structure, as it tunes the relative energetic positions between the K and the Γ valleys of the valence band maximum of the untwisted bilayer. As a result, applying uniaxial pressure to a twisted bilayer induces a charge-transfer from the K valley to the flat bands in the Γ valley. Upon Wannierizing moiré bands from both valleys, we establish the relevant tight-binding model parameters and calculate the effective interaction strengths using the constrained random phase approximation. With this, we approximate the interacting pressure-doping phase diagram of WSe2 moiré bilayers using self-consistent mean field theory. Our results establish twisted bilayer WSe2 as a platform that allows the direct pressure-tuning of different correlated phases, ranging from Mott insulators, charge-valley-transfer insulators to Kondo lattice-like systems.

A New Multimodal Modification of the Skew Family of Distributions: Properties and Applications to Medical and Environmental Data

The skew distribution has the characteristic of appropriately modeling asymmetric unimodal data. However, in practice, there are several cases in which the data present more than one mode. In the literature, it is possible to find a large number of authors who have studied extensions based on the skew distribution to model this type of data. In this article, a new family is introduced, consisting of a multimodal modification to the family of skew distributions. Using the methodology of the weighted version of a function, we perform the product of the density function of a family of skew distributions with a polynomial of degree 4, thus obtaining a more flexible model that allows modeling data sets, whose distribution contains at most three modes. The density function, some properties, moments, skewness coefficients, and kurtosis of this new family are presented. This study focuses on the particular cases of skew-normal and Laplace distributions, although it can be applied to any other distribution. A simulation study was carried out, to study the behavior of the model parameter estimates. Illustrations with real data, referring to medicine and environmental data, show the practical performance of the proposed model in the two particular cases presented.

A system of ODEs for representing trends of CGM signals

Diabetes Mellitus is a metabolic disorder which may result in severe and potentially fatal complications if not well-treated and monitored. In this study, a quantitative analysis of the data collected using CGM (Continuous Glucose Monitoring) devices from eight subjects with type 2 diabetes in good metabolic control at the University Polyclinic Agostino Gemelli, Catholic University of the Sacred Heart, was carried out. In particular, a system of ordinary differential equations whose state variables are affected by a sequence of stochastic perturbations was proposed and used to extract more informative inferences from the patients’ data. For this work, Matlab and R programs were used to find the most appropriate values of the parameters (according to the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC)) for each patient. Fitting was carried out by Particle Swarm Optimization to minimize the ordinary least squares error between the observed CGM data and the data from the ODE model. Goodness of fit tests were made in order to assess which probability distribution was best suitable for representing the waiting times computed from the model parameters. Finally, both parametric and non-parametric density estimation of the frequency histograms associated with the variability of the glucose elimination rate from blood were conducted and their representative parameters assessed from the data. The results show that the chosen models succeed in capturing most of the glucose fluctuations for almost every patient.

Linear Source Apportionment Using Generalized Least Squares

Motivated by applications to water quality monitoring using fluorescence spectroscopy, we develop the source apportionment model for high dimensional profiles of dissolved organic matter (DOM). We describe simple methods to estimate the parameters of a linear source apportionment model, and show how the estimates are related to those of ordinary and generalized least squares. Using this least squares framework, we analyze the variability of the estimates, and we propose predictors for missing elements of a DOM profile. We demonstrate the practical utility of our results on fluorescence spectroscopy data collected from the Neuse River in North Carolina.

Copula Approximate Bayesian Computation Using Distribution Random Forests

Ongoing modern computational advancements continue to make it easier to collect increasingly large and complex datasets, which can often only be realistically analyzed using models defined by intractable likelihood functions. This Stats invited feature article introduces and provides an extensive simulation study of a new approximate Bayesian computation (ABC) framework for estimating the posterior distribution and the maximum likelihood estimate (MLE) of the parameters of models defined by intractable likelihoods, that unifies and extends previous ABC methods proposed separately. This framework, copulaABCdrf, aims to accurately estimate and describe the possibly skewed and high-dimensional posterior distribution by a novel multivariate copula-based meta-t distribution based on univariate marginal posterior distributions that can be accurately estimated by distribution random forests (drf), while performing automatic summary statistics (covariates) selection, based on robustly estimated copula dependence parameters. The copulaABCdrf framework also provides a novel multivariate mode estimator to perform MLE and posterior mode estimation and an optional step to perform model selection from a given set of models using posterior probabilities estimated by drf. The posterior distribution estimation accuracy of the ABC framework is illustrated and compared with previous standard ABC methods through several simulation studies involving low- and high-dimensional models with computable posterior distributions, which are either unimodal, skewed, or multimodal; and exponential random graph and mechanistic network models, each defined by an intractable likelihood from which it is costly to simulate large network datasets. This paper also proposes and studies a new solution to the simulation cost problem in ABC involving the posterior estimation of parameters from datasets simulated from the given model that are smaller compared to the potentially large size of the dataset being analyzed. This proposal is motivated by the fact that, for many models defined by intractable likelihoods, such as the network models when they are applied to analyze massive networks, the repeated simulation of large datasets (networks) for posterior-based parameter estimation can be too computationally costly and vastly slow down or prohibit the use of standard ABC methods. The copulaABCdrf framework and standard ABC methods are further illustrated through analyses of large real-life networks of sizes ranging between 28,000 and 65.6 million nodes (between 3 million and 1.8 billion edges), including a large multilayer network with weighted directed edges. The results of the simulation studies show that, in settings where the true posterior distribution is not highly multimodal, copulaABCdrf usually produced similar point estimates from the posterior distribution for low-dimensional parametric models as previous ABC methods, but the copula-based method can produce more accurate estimates from the posterior distribution for high-dimensional models, and, in both dimensionality cases, usually produced more accurate estimates of univariate marginal posterior distributions of parameters. Also, posterior estimation accuracy was usually improved when pre-selecting the important summary statistics using drf compared to ABC employing no pre-selection of the subset of important summaries. For all ABC methods studied, accurate estimation of a highly multimodal posterior distribution was challenging. In light of the results of all the simulation studies, this article concludes by discussing how the copulaABCdrf framework can be improved for future research.

Translational modelling to predict human pharmacokinetics and pharmacodynamics of a Bruton's tyrosine kinase-targeted protein degrader BGB-16673.

Bifunctional small molecule degraders, which link the target protein with E3 ubiquitin ligase, could lead to the efficient degradation of the target protein. BGB-16673 is a Bruton's tyrosine kinase (BTK) degrader. A translational PK/PD modelling approach was used to predict the human BTK degradation of BGB-16673 from preclinical in vitro and in vivo data. A simplified mechanistic PK/PD model was used to establish the correlation between the in vitro and in vivo BTK degradation by BGB-16673 in a mouse model. Human and mouse species differences were compared using the parameters generated from in vitro human or mouse blood, and human or mouse serum spiked TMD-8 cells. Human PD was then predicted using the simplified mechanistic PK/PD model. BGB-16673 showed potent BTK degradation in mouse whole blood, human whole blood, and TMD-8 tumour cells in vitro. Furthermore, BGB-16673 showed BTK degradation in a murine TMD-8 xenograft model in vivo. The PK/PD model predicted human PD and the observed BTK degradation in clinical studies both showed robust BTK degradation in blood and tumour at clinical dose range. The presented simplified mechanistic model with reduced number of model parameters is practically easier to be applied to research projects compared with the full mechanistic model. It can be used as a tool to better understand the PK/PD behaviour for targeted protein degraders and increase the confidence when moving to the clinical stage.

Model‐based clustering in simple hypergraphs through a stochastic blockmodel

AbstractWe propose a model to address the overlooked problem of node clustering in simple hypergraphs. Simple hypergraphs are suitable when a node may not appear multiple times in the same hyperedge, such as in co‐authorship datasets. Our model generalizes the stochastic blockmodel for graphs and assumes the existence of latent node groups and hyperedges are conditionally independent given these groups. We first establish the generic identifiability of the model parameters. We then develop a variational approximation Expectation‐Maximization algorithm for parameter inference and node clustering, and derive a statistical criterion for model selection. To illustrate the performance of our R package HyperSBM, we compare it with other node clustering methods using synthetic data generated from the model, as well as from a line clustering experiment and a co‐authorship dataset.